DependenceThresholdRedundancy#

class skfda.preprocessing.dim_reduction.variable_selection.recursive_maxima_hunting.DependenceThresholdRedundancy(threshold=0.9, *, dependence_measure=<function u_distance_correlation_sqr>)[source]#

The points are redundant if their dependency is above a given threshold.

This stopping condition requires that the dependency has a known bound, for example that it takes values in the interval \([0, 1]\).

Parameters:

threshold (float) – Value compared with the score. If the score of the selected point is not higher than that, the point will not be selected (unless it is the first iteration) and RMH will end.
dependence_measure (_DepMeasure[NDArrayFloat, NDArrayFloat]) – Dependence measure to use. By default, it uses the bias corrected squared distance correlation.

Methods

`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`set_params`(**params)	Set the parameters of this estimator.

get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:: routing – A MetadataRequest encapsulating routing information.
Return type:: MetadataRequest

get_params(deep=True)#

Get parameters for this estimator.

Parameters:: deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:: params – Parameter names mapped to their values.
Return type:: dict

set_params(**params)#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:: **params (dict) – Estimator parameters.
Returns:: self – Estimator instance.
Return type:: estimator instance